IMO:TSX Imperial Oil Limited Research Report

Outlook: Imperial Oil Limited is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Sell
Time series to forecast n: 07 Jan 2023 for (n+4 weeks)
Methodology : Multi-Instance Learning (ML)

Abstract

Imperial Oil Limited prediction model is evaluated with Multi-Instance Learning (ML) and Pearson Correlation^1,2,3,4 and it is concluded that the IMO:TSX stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

Key Points

1. What is prediction in deep learning?
2. Can neural networks predict stock market?
3. How do you know when a stock will go up or down?

IMO:TSX Target Price Prediction Modeling Methodology

We consider Imperial Oil Limited Decision Process with Multi-Instance Learning (ML) where A is the set of discrete actions of IMO:TSX stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^1,2,3,4

F(Pearson Correlation)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Multi-Instance Learning (ML)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of IMO:TSX stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do AC Investment Research machine learning (predictive) algorithms actually work?

IMO:TSX Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: IMO:TSX Imperial Oil Limited
Time series to forecast n: 07 Jan 2023 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

IFRS Reconciliation Adjustments for Imperial Oil Limited

1. Credit risk analysis is a multifactor and holistic analysis; whether a specific factor is relevant, and its weight compared to other factors, will depend on the type of product, characteristics of the financial instruments and the borrower as well as the geographical region. An entity shall consider reasonable and supportable information that is available without undue cost or effort and that is relevant for the particular financial instrument being assessed. However, some factors or indicators may not be identifiable on an individual financial instrument level. In such a case, the factors or indicators should be assessed for appropriate portfolios, groups of portfolios or portions of a portfolio of financial instruments to determine whether the requirement in paragraph 5.5.3 for the recognition of lifetime expected credit losses has been met.
2. An entity's documentation of the hedging relationship includes how it will assess the hedge effectiveness requirements, including the method or methods used. The documentation of the hedging relationship shall be updated for any changes to the methods (see paragraph B6.4.17).
3. Lifetime expected credit losses are generally expected to be recognised before a financial instrument becomes past due. Typically, credit risk increases significantly before a financial instrument becomes past due or other lagging borrower-specific factors (for example, a modification or restructuring) are observed. Consequently when reasonable and supportable information that is more forward-looking than past due information is available without undue cost or effort, it must be used to assess changes in credit risk.
4. For the purpose of applying paragraphs B4.1.11(b) and B4.1.12(b), irrespective of the event or circumstance that causes the early termination of the contract, a party may pay or receive reasonable compensation for that early termination. For example, a party may pay or receive reasonable compensation when it chooses to terminate the contract early (or otherwise causes the early termination to occur).

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

Imperial Oil Limited is assigned short-term Ba1 & long-term Ba1 estimated rating. Imperial Oil Limited prediction model is evaluated with Multi-Instance Learning (ML) and Pearson Correlation^1,2,3,4 and it is concluded that the IMO:TSX stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Sell

IMO:TSX Imperial Oil Limited Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	C	B2
Balance Sheet	B1	Baa2
Leverage Ratios	Baa2	B3
Cash Flow	Caa2	Baa2
Rates of Return and Profitability	Caa2	B3

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 73 out of 100 with 814 signals.

References

1. Belsley, D. A. (1988), "Modelling and forecast reliability," International Journal of Forecasting, 4, 427–447.
2. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
3. Jacobs B, Donkers B, Fok D. 2014. Product Recommendations Based on Latent Purchase Motivations. Rotterdam, Neth.: ERIM
4. Çetinkaya, A., Zhang, Y.Z., Hao, Y.M. and Ma, X.Y., MO Stock Price Prediction. AC Investment Research Journal, 101(3).
5. Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot, and N. de Freitas. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 1995–2003, 2016.
6. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
7. Friedman JH. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–78

Frequently Asked Questions

Q: What is the prediction methodology for IMO:TSX stock?
A: IMO:TSX stock prediction methodology: We evaluate the prediction models Multi-Instance Learning (ML) and Pearson Correlation
Q: Is IMO:TSX stock a buy or sell?
A: The dominant strategy among neural network is to Sell IMO:TSX Stock.
Q: Is Imperial Oil Limited stock a good investment?
A: The consensus rating for Imperial Oil Limited is Sell and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of IMO:TSX stock?
A: The consensus rating for IMO:TSX is Sell.
Q: What is the prediction period for IMO:TSX stock?
A: The prediction period for IMO:TSX is (n+4 weeks)

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